A new input estimation (IE) model for problems in tracking manoeuvring targets is proposed. The proposed model is constructed by combining the two models of uncertainties, Bayesian and Fisher. The conventional model, which describes targets with manoeuvre, is based on the state vector of target position and velocity. The acceleration is treated as an additive input term in the corresponding state equation. The proposed method is a Kalman filter-based tracking scheme with the IE approach. The proposed model is a special augmentation in the state-space model which considers both the state vector and the unknown input vector as a new augmented state vector. In the proposed scheme, the original state and acceleration vectors are estimated simultaneously with a standard Kalman filter. The proposed tracking algorithm operates in both the non-manoeuvring and the manoeuvring modes and the manoeuvre detection procedure is eliminated. The theoretical development is verified by simulation results, which also contain some examples of tracking typical target manoeuvres. The results are compared with a traditional IE method. A comparison based on the Monte-Carlo simulation is also made to evaluate the performances of the proposed method in three scenarios: low, medium and high manoeuvring target.

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This paper exhibits a comparative assessment based on time response specification performance between modern and classical controller for a pitch control system of an aircraft system. The dynamic modeling of pitch control system is considered on the design of an autopilot that controls the pitch angle. It starts with a derivation of a suitable mathematical model to describe the dynamics of an aircraft. For getting close to actual conditions the white noise disturbance is applied to the system. In this paper, it is assumed that the model pitch control system is not available. So using the identification system and Box-Jenkins model estimator we identify the pitch control system. System's identification is a procedure for accurately characterizing the dynamic response behavior of a complete aircraft, of a subsystem, or of an individual component from measured data. To study the effectiveness of the controllers, the LQR Controller and PID Controller and fuzzy controller is developed for controlling the pitch angle of an aircraft system. Simulation results for the response of pitch controller are presented instep's response. Finally, the performances of pitch control systems are investigated and analyzed based on common criteria of step's response in order to identify which control strategy delivers better performance with respect to the desired pitch angle. It is found from simulation that the fuzzy controller gives the best performance compared to PID and LQR controller.